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README.md
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# PurpleBoW
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A very simple implementation of Bag-of-Words for those learning about Natural Language Processing.
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Essentially, we can train a model to remember a set of words we call ordered vocabulary to later count each words from a paragraph or sentence.
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The resulting "prediction" is a vector of ordered counts of those words and their position doesn't matter.
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This is quite good for simple detection, like spam emails which contains a lot of "quick", "win", or "prizes" word.
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However, when it comes to positional meaning BoW performs very poorly. It's like instructing a gold fish to climb a coconut tree.
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---
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license: wtfpl
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datasets:
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- ShoAnn/legalqa_klinik_hukumonline
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language:
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- id
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pipeline_tag: feature-extraction
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---
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# PurpleBoW
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A very simple implementation of Bag-of-Words for those learning about Natural Language Processing.
|
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|
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Essentially, we can train a model to remember a set of words we call ordered vocabulary to later count each words from a paragraph or sentence.
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The resulting "prediction" is a vector of ordered counts of those words and their position doesn't matter.
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This is quite good for simple detection, like spam emails which contains a lot of "quick", "win", or "prizes" word.
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However, when it comes to positional meaning BoW performs very poorly. It's like instructing a gold fish to climb a coconut tree.
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